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Non-Local and Multi-Scale Mechanisms for Image Inpainting
1School of Microelectronics and Communication Engineering, Chongqing University, Chongqing 400044, China.
Sensors (Basel, Switzerland)
|June 2, 2021
Summary
This study introduces a novel deep learning framework for advanced image inpainting, effectively restoring large, irregular missing areas. The method combines contextual attention and diversified Markov random fields for superior visual quality and detail accuracy.
Area of Science:
- Computer Vision
- Artificial Intelligence
- Deep Learning
Background:
- Deep learning excels at image inpainting for square holes but struggles with irregular, large missing regions due to poor contextual understanding.
- Existing methods lack the ability to effectively bridge the semantic gap between missing and surrounding image areas.
Purpose of the Study:
- To develop an advanced image inpainting framework capable of restoring large, irregular masked areas with high fidelity.
- To enhance the understanding of relationships between missing and existing image regions for more plausible restorations.
Main Methods:
- A multi-scale architecture utilizing dense fusion blocks (DFB) with dilated convolutions for expanded receptive fields.
- Integration of a contextual attention module (CAM) to capture long-range dependencies within large missing regions.
- Application of an implicit diversified Markov random fields (ID-MRF) loss to ensure visual coherence and prevent artifacts like color discrepancies and grid patterns.
Main Results:
- The proposed framework successfully restores discontinuous and continuous large masked areas, outperforming state-of-the-art methods.
- ID-MRF loss significantly improves visual appearance by preserving long-distance feature patch similarities.
- DFB enhances CAM's performance by providing more precise input features, leading to finer-grained restorations.
Conclusions:
- The novel framework effectively addresses limitations in deep learning-based image inpainting for complex missing regions.
- The combination of CAM, ID-MRF loss, and DFB-powered multi-scale architecture offers a significant advancement in image restoration quality and quantity.
